Path Planning for Unmanned Aerial Vehicles in Constrained Environments for Locust Elimination

EasyChair Preprint no. 6337

Abstract

Present-day agricultural practices such as blanket spraying not only leads to excessive usage of pesticides, but also harms the overall crop yield. This paper introduces an algorithm to optimize the traversal of an Unmanned Aerial Vehicle (UAV) in constrained environments. The proposed system focuses on the agricultural application of targeted spraying for locust elimination. Given a satellite image of a farm, target zones that are prone to locust swarm formation are detected through calculation of the Normalized Difference Vegetation Index (NDVI). This is followed by determining the optimal path for traversal of a UAV through these target zones using the proposed algorithm in order to perform pesticide spraying in the most efficient manner possible. Unlike the classic travelling salesman problem involving point-to-point optimization, the proposed algorithm determines an optimal path for multiple regions, independent of its geometry. The savings obtained by employing the proposed method is directly proportional to the total non-infested area in an agricultural land compared to the conventional method. Finally, the paper explores the idea of implementing reinforcement learning to model complex environmental behaviour and make the path planning mechanism for UAVs agnostic to external environment changes. This system not only presents a solution to the enormous losses incurred due to locust attacks, but also an efficient way to automate agricultural practices across the globe in order to improve farmer ergonomics.

Keyphrases: multispectral image, Normalized Difference Vegetation Index, Optimization, path planning, Reinforcement Learning, Targeted spraying, Unmanned Aerial Vehicle

@Booklet{EasyChair:6337,
  author = {Aadiv Shah and Hari Nair and Vedant Mittal and Alice Cheeran},
  title = {Path Planning for Unmanned Aerial Vehicles in Constrained Environments for Locust Elimination},
  howpublished = {EasyChair Preprint no. 6337},

  year = {EasyChair, 2021}}